Editor’s Note: This story is part of a series of notable alumni profiles for Spring 2023.
How do you make sense of the artificial intelligence story?
casey hatfield
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Kacy Hatfield is a student at the Herberger Institute for Design and the Arts, who writes children’s books aiming to make AI stories accessible to all. Casey graduated with a degree in Digital Culture this May and is an undergraduate fellow at the Lincoln Center for Applied Ethics.
After graduating, she completed her master’s degree and was invited to join Draper Labs’ Machine Intelligence Group.
She shared more about her college journey below.
Q: Tell us about your experience at ASU and how you came to study digital culture.
answer: I actually came to ASU as a biochemistry major. I love chemistry and mathematics but the career path was not what I had hoped for. Then I explored career and creative work opportunities where I found digital culture and was hooked in just 3 days. I switched. And there are still many things I love about AI research that allow me to integrate my love of chemistry and mathematics into it.
I actually hadn’t heard of machine learning until the spring of 2021. After her professor introduced her to machine learning, I asked her for her book recommendations. Since then, I’ve been obsessed with her AI.
Q: What made you want to start undergraduate research?
A: In fact, I wrote my honors thesis shortly after learning about AI and machine learning. I didn’t know much about the subject, but decided I wanted to pursue it and pitched it to some professors who wanted to work with me. I defended my paper just a year after he first learned about machine learning, but I had a great time working on a paper that made me want to continue my research.
Then I found the Lincoln Center for Applied Ethics. There was an opportunity for an undergraduate study on responsible AI. At Zoom, the Research Program Manager met with Erica O’Neil and thought it would be the perfect continuation of my work. It would be great to continue researching and learning about this, but eventually more questions will arise.
Q: You are working on a very exciting project, developing a children’s book about AI. Could you tell us more about this project?
A: The premise is a picture book that tells the story of an algorithm (like commands in the Python programming language) named Pip, which must sort seashells on a beach. The way Pip categorizes them starts with very simple terms, and more advanced terms become apparent as the waves wash ashore. There’s also a character named epoch (another term in Python) and a character representing the human in the loop. They are all very strategically placed to represent what would happen if machine learning algorithms were integrated into this realm.
The goal is to make people feel less scared of machine learning. I often see AI described as a black box. What people cannot see or understand. But I think the test of a good machine learning algorithm (and a good programmer) is to translate that black box into something easily understood.
One of the reasons I love machine learning is that you can spend your whole life studying AI and never fully understand it. I think that’s the key to why people feel uncertain about machine learning. Especially when it’s the humanoid robots that the Hollywood saga of AI takes over. The problem is that these technologies are immoral and not immoral.
My goal as a researcher is to assuage skepticism about machine learning through this book. And while this starts with young people, the book is also meant to be used by people of all ages.
Q: How does your time spent in the Responsible AI research group relate to your work?
A: I love being in this research group. It’s actually my second semester of him. Last semester, I did a project on risk and mitigation for AI-powered autonomous spacecraft. This is another concern of mine. It’s great to be part of a group of people with different backgrounds and different approaches to AI. A great many interdisciplinary perspectives and topics are covered in the discussion.
From a responsible AI perspective, many may disagree with me on this, but it’s also essential that programmers be able to understand the ethical implications of what they’re adopting into the world. It is often argued that we should wait five years before assessing the potential for these effects. When I am working on programming, I am immediately thinking about how it will affect the real world and how it will be used.
Machine learning is like a mirror. Whatever is given is reflected, but humans are not perfect. This is why I think any discussion of ethics should go hand-in-hand with the research itself, and it will be very interesting to see how it plays out on all fronts.
Q: What are your next steps in your career and future?
A: That’s an old question. I always have a list of problems I can research! I may be a geeky confession, but I love doing research even in my spare time. I would like to channel that energy towards my master’s degree and possibly my Ph.D. I have also been invited to join Draper Labs’ Machine Intelligence Group as an Undergraduate Engineer in the summer of 2023, which is a very exciting opportunity.
The great thing about this field is that it’s always changing. In a way, you can always feel like a student. Also, since AI research is so new, I believe that taking an ethical and programming approach at the same time would be much easier to integrate than the already established ones. We will continue to maintain these skills as best practices.
There is a lot of skepticism about AI and machine learning, and we often hear that it is either too complicated or too complicated. Everyone has the ability to understand AI, and it’s not as scary as it seems. Machine learning is heavily skewed toward entertainment, which makes it easy to vilify, but there are so many benefits to using machine learning that it can be used in the right way to extend the human experience without hindering it. You can use machine learning.
